SummaryIn most European countries social insurance programs, like welfare, unemployment insurance and disability insurance are characterized by low reemployment rates. Therefore, governments spend huge amounts of money on active labour market programs, which should help individuals in finding work. Recent surveys indicate that programs which aim at intensifying job search behaviour are much more effective than schooling programs for improving human capital. A second conclusion from these surveys is that despite the size of the spendings on these programs, evidence on its effectiveness is limited. This research proposal aims at developing an economic framework that will be used to evaluate the effectiveness of popular programs like offering reemployment bonuses, fraud detection, workfare and job search monitoring. The main innovation is that I will combine economic theory with recently developed econometric techniques and detailed administrative data sets, which have not been explored before. While most of the literature only focuses on short-term outcomes, the available data allow me to also consider the long-term effectiveness of programs. The key advantage of an economic model is that I can compare the effectiveness of the different programs, consider modifications of programs and combinations of programs. Furthermore, using an economic model I can construct profiling measures to improve the targeting of programs to subsamples of the population. This is particularly relevant if the effectiveness of programs differs between individuals or depends on the moment in time the program is offered. Therefore, the results from this research will not only be of scientific interest, but will also be of great value to policymakers.

In most European countries social insurance programs, like welfare, unemployment insurance and disability insurance are characterized by low reemployment rates. Therefore, governments spend huge amounts of money on active labour market programs, which should help individuals in finding work. Recent surveys indicate that programs which aim at intensifying job search behaviour are much more effective than schooling programs for improving human capital. A second conclusion from these surveys is that despite the size of the spendings on these programs, evidence on its effectiveness is limited. This research proposal aims at developing an economic framework that will be used to evaluate the effectiveness of popular programs like offering reemployment bonuses, fraud detection, workfare and job search monitoring. The main innovation is that I will combine economic theory with recently developed econometric techniques and detailed administrative data sets, which have not been explored before. While most of the literature only focuses on short-term outcomes, the available data allow me to also consider the long-term effectiveness of programs. The key advantage of an economic model is that I can compare the effectiveness of the different programs, consider modifications of programs and combinations of programs. Furthermore, using an economic model I can construct profiling measures to improve the targeting of programs to subsamples of the population. This is particularly relevant if the effectiveness of programs differs between individuals or depends on the moment in time the program is offered. Therefore, the results from this research will not only be of scientific interest, but will also be of great value to policymakers.

Max ERC Funding

550 000 €

Duration

Start date: 2008-07-01, End date: 2013-06-30

Project acronymBayesianMarkets

ProjectBayesian markets for unverifiable truths

Researcher (PI)Aurelien Baillon

Host Institution (HI)ERASMUS UNIVERSITEIT ROTTERDAM

Call DetailsStarting Grant (StG), SH1, ERC-2014-STG

SummarySubjective data play an increasing role in modern economics. For instance, new welfare measurements are based on people’s subjective assessments of their happiness or their life satisfaction. A problem of such measurements is that people have no incentives to tell the truth. To solve this problem and make those measurements incentive compatible, I will introduce a new market institution, called Bayesian markets.
Imagine we ask people whether they are happy with their life. On Bayesian markets, they will trade an asset whose value is the proportion of people answering Yes. Only those answering Yes will have the right to buy the asset and those answering No the right to sell it. Bayesian updating implies that “Yes” agents predict a higher value of the asset than “No” agents do and, consequently, “Yes” agents want to buy it while “No” agents want to sell it. I will show that truth-telling is then the optimal strategy.
Bayesian markets reward truth-telling the same way as prediction markets (betting markets) reward people for reporting their true subjective probabilities about observable events. Yet, unlike prediction markets, they do not require events to be objectively observable. Bayesian markets apply to any type of unverifiable truths, from one’s own happiness to beliefs about events that will never be observed.
The present research program will first establish the theoretical foundations of Bayesian markets. It will then develop the proper methodology to implement them. Finally, it will disseminate the use of Bayesian markets via applications.
The first application will demonstrate how degrees of expertise can be measured and will apply it to risks related to climate change and nuclear power plants. It will contribute to the political debate by shedding new light on what true experts think about these risks. The second application will provide the first incentivized measures of life satisfaction and happiness.

Subjective data play an increasing role in modern economics. For instance, new welfare measurements are based on people’s subjective assessments of their happiness or their life satisfaction. A problem of such measurements is that people have no incentives to tell the truth. To solve this problem and make those measurements incentive compatible, I will introduce a new market institution, called Bayesian markets.
Imagine we ask people whether they are happy with their life. On Bayesian markets, they will trade an asset whose value is the proportion of people answering Yes. Only those answering Yes will have the right to buy the asset and those answering No the right to sell it. Bayesian updating implies that “Yes” agents predict a higher value of the asset than “No” agents do and, consequently, “Yes” agents want to buy it while “No” agents want to sell it. I will show that truth-telling is then the optimal strategy.
Bayesian markets reward truth-telling the same way as prediction markets (betting markets) reward people for reporting their true subjective probabilities about observable events. Yet, unlike prediction markets, they do not require events to be objectively observable. Bayesian markets apply to any type of unverifiable truths, from one’s own happiness to beliefs about events that will never be observed.
The present research program will first establish the theoretical foundations of Bayesian markets. It will then develop the proper methodology to implement them. Finally, it will disseminate the use of Bayesian markets via applications.
The first application will demonstrate how degrees of expertise can be measured and will apply it to risks related to climate change and nuclear power plants. It will contribute to the political debate by shedding new light on what true experts think about these risks. The second application will provide the first incentivized measures of life satisfaction and happiness.

SummaryIn economics, a distinction is made between statistical and taste-based discrimination (henceforth, TBD). Statistical discrimination refers to discrimination in a context with strategic uncertainty. Someone who is uncertain about the future behaviour of a person with a different ethnicity may rely on information about the different ethnic group to which this person belongs to form beliefs about the behaviour of that person. This may lead to discrimination. TBD refers to discrimination in a context without strategic uncertainty. It implies suffering a disutility when interacting with ‘different’ others. This project systematically studies TBD in ethnically diverse societies.
Identifying TBD is important because overcoming it requires different policies than overcoming statistical discrimination: they should deal with changing preferences of people rather than providing information about specific interaction partners. But identifying TBD is tricky. First, it is impossible to identify using uncontrolled empirical data because these data are characterised by strategic uncertainty. Second, people are generally reluctant to identify themselves as a discriminator. In the project, I study TBS using novel economic experiments that circumvent these problems.
The project consists of three main objectives. First, I investigate whether and how preferences of European natives in social interactions depend on others’ ethnicity. Are natives as altruistic, reciprocal, envious to immigrants as compared to other natives? Second, I study whether natives have different fairness ideals—what constitutes a fair distribution of resources from the perspective of an impartial spectator—when it comes to natives than when it comes to non-natives. Third, I analyse whether preferences and fairness ideals depend on exposure to diversity: do preferences and fairness ideals of natives change as contact with non-natives increases, and, if so, how?

In economics, a distinction is made between statistical and taste-based discrimination (henceforth, TBD). Statistical discrimination refers to discrimination in a context with strategic uncertainty. Someone who is uncertain about the future behaviour of a person with a different ethnicity may rely on information about the different ethnic group to which this person belongs to form beliefs about the behaviour of that person. This may lead to discrimination. TBD refers to discrimination in a context without strategic uncertainty. It implies suffering a disutility when interacting with ‘different’ others. This project systematically studies TBD in ethnically diverse societies.
Identifying TBD is important because overcoming it requires different policies than overcoming statistical discrimination: they should deal with changing preferences of people rather than providing information about specific interaction partners. But identifying TBD is tricky. First, it is impossible to identify using uncontrolled empirical data because these data are characterised by strategic uncertainty. Second, people are generally reluctant to identify themselves as a discriminator. In the project, I study TBS using novel economic experiments that circumvent these problems.
The project consists of three main objectives. First, I investigate whether and how preferences of European natives in social interactions depend on others’ ethnicity. Are natives as altruistic, reciprocal, envious to immigrants as compared to other natives? Second, I study whether natives have different fairness ideals—what constitutes a fair distribution of resources from the perspective of an impartial spectator—when it comes to natives than when it comes to non-natives. Third, I analyse whether preferences and fairness ideals depend on exposure to diversity: do preferences and fairness ideals of natives change as contact with non-natives increases, and, if so, how?

Max ERC Funding

1 499 046 €

Duration

Start date: 2018-01-01, End date: 2022-12-31

Project acronymEdGe

ProjectThe molecular genetic architecture of educational attainment and its significance for cognitive health

Researcher (PI)Philipp Daniel Koellinger

Host Institution (HI)STICHTING VU

Call DetailsConsolidator Grant (CoG), SH1, ERC-2014-CoG

SummarySince many social and economic outcomes are moderately heritable, it is in principle possible to discover genetic variants associated with them. Such discoveries could yield new insights into the causal pathways underlying human behaviour, the complex interplay of environmental and genetic factors, and the relationship between socio-economic traits and health.
This proposal builds on a recent genome-wide association study on educational attainment (EA) led by the applicant (Rietveld et al. 2013, Science), which identified for the first time specific genetic variants robustly associated with a socio-economic outcome. The project will leverage the unique resources of the Social Science Genetic Association Consortium (SSGAC), which is co-led by the applicant.
The proposed research will extend existing knowledge by: 1) discovering additional genetic variants and causal pathways associated with EA; 2) developing methods to use the available genetic association results in novel, more efficient ways; 3) shedding new light on characteristics related to EA such as economic preferences, cognitive function, and cognitive health; 4) showing how policies promoting EA interact with genetic predisposition; 5) using genetic information to better understand the causal effects of educational policy interventions, 6) developing better tools to identify individuals at risk for cognition-related diseases before the onset of symptoms; and 7) identifying causal pathways of genetic influence on cognitive health via neurobiological measures. The project aims to elucidate the complex causal pathways connecting genes, environment, individual characteristics, and health-related outcomes; make methodological contributions applicable in genetic epidemiology and the social sciences; and contribute towards designing more effective public policy, which could improve public health and lower health costs.

Since many social and economic outcomes are moderately heritable, it is in principle possible to discover genetic variants associated with them. Such discoveries could yield new insights into the causal pathways underlying human behaviour, the complex interplay of environmental and genetic factors, and the relationship between socio-economic traits and health.
This proposal builds on a recent genome-wide association study on educational attainment (EA) led by the applicant (Rietveld et al. 2013, Science), which identified for the first time specific genetic variants robustly associated with a socio-economic outcome. The project will leverage the unique resources of the Social Science Genetic Association Consortium (SSGAC), which is co-led by the applicant.
The proposed research will extend existing knowledge by: 1) discovering additional genetic variants and causal pathways associated with EA; 2) developing methods to use the available genetic association results in novel, more efficient ways; 3) shedding new light on characteristics related to EA such as economic preferences, cognitive function, and cognitive health; 4) showing how policies promoting EA interact with genetic predisposition; 5) using genetic information to better understand the causal effects of educational policy interventions, 6) developing better tools to identify individuals at risk for cognition-related diseases before the onset of symptoms; and 7) identifying causal pathways of genetic influence on cognitive health via neurobiological measures. The project aims to elucidate the complex causal pathways connecting genes, environment, individual characteristics, and health-related outcomes; make methodological contributions applicable in genetic epidemiology and the social sciences; and contribute towards designing more effective public policy, which could improve public health and lower health costs.